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Record W4386272536 · doi:10.1080/14737175.2023.2251687

Oral Edaravone – Introducing a Flexible Treatment Option for Amyotrophic Lateral Sclerosis

2023· review· en· W4386272536 on OpenAlexaff
Gary L. Pattee, Angela Genge, Philippe Couratier, Christian Lunetta, Gen Sobue, Masashi Aoki, Hiide Yoshino, Carlayne E. Jackson, James Wymer, Alejandro Salah, Sally Nelson

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2023
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMontreal Neurological Institute and Hospital
FundersMitsubishi Tanabe Pharma Corporation
KeywordsAmyotrophic lateral sclerosisEdaravoneMedicineNeurosciencePhysical medicine and rehabilitationAnesthesiaPsychologyInternal medicineDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive and incurable neurodegenerative disease. While pharmacotherapy options remain limited, the Food and Drug Administration (FDA) approved intravenous (IV) and oral edaravone for the treatment of ALS in 2017 and 2022, respectively. With the addition of oral edaravone, patients with ALS may exclusively use oral medications. AREAS COVERED: The authors performed a review of the published literature using the United States (US) National Library of Medicine's PubMed.gov resource to describe the pharmacokinetics, pharmacodynamics, safety, and efficacy of oral edaravone, as well as pertinent completed and ongoing clinical trials, including the oral edaravone clinical trial development program. The clinical profile of oral edaravone is also discussed. EXPERT OPINION: Edaravone has been shown to slow the rate of motor function deterioration experienced by patients with ALS. As the oral formulation has been approved, patients with ALS may use it alone or in combination with other approved therapeutics. Additional clinical trials and real-world evidence are ongoing to gain further understanding of the clinical profile of oral edaravone.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.321
GPT teacher head0.481
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2023
Admission routes1
Has abstractyes

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